Statistics and Computing
This page provides some information about the statistical applications and computing resources used in Dr. Rosopa’s research lab.
R is an open-source statistical environment. R is based on the S language developed at Bell Labs. R is flexible and powerful (e.g., modern statistical methods, dynamically interface with compiled routines in C, FORTRAN, C++, interface with Java methods, highly extensible). Although R does not have a graphical user interface like that of other statistical environments, John Fox (a sociologist and statistician) has developed an add-on package which loads a graphical user interface in R. Dr. Rosopa uses R for research (e.g., Monte Carlo simulations) and teaching. Being an open-source system, there is an international community of researchers in statistics, computer science, psychometrics, and other areas that contribute to the continued development of R. It is not uncommon for new methods to become available in R much faster than in other statistical applications.
S and R are used in a variety of industries and research disciplines. To become more familiar with R, here are some useful links.
- Econometrics (UC-Berkeley Econometrics Laboratory)
- Environmental Statistics (Philip Dixon)
- Political Science (Jeff Gill)
- Psychology (R for Psychology)
- Quantitative Risk Management (Alexander J. McNeil)
- Sociology (John Fox)
- Statistics (UCLA, Basics of S-PLUS, Robust Methods by Rand Wilcox)
Here are some recommended books:
- Faraway, J. J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.
- Fox, J. (2002). An R and S-PLUS Companion to Applied Regression.
- Krause, A., & Olson, M. (2005). The Basics of S-PLUS (4th ed.).
- Pinheiro, J. C., & Bates, D. M. (2000). Mixed Effects Models in S and S-PLUS.
- Venables, W., & Ripley, B. D. (2000). S Programming.
- Venables, W., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th ed.).
Dr. Rosopa has used Clemson University's Palmetto Cluster to complete simulations in weeks that would have taken years to complete. Researchers have used the Palmetto Cluster for various purposes including machine learning, simulation of polymer blends, and genetic algorithms for large scale optimization in manufacturing.